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RuntimeError: [VPU] Unsupported precision I32for data 37587_const

yu__jia
Beginner
‎12-09-2019 02:29 AM
517 Views

Hello,everyone ,I retrained Inceptionv1 using this link,

https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models

and converted the saved .pb to IR with the following command.

C：\ Program Files（x86）\ IntelSWTools \ openvino \ deployment_tools \ model_optimizer> python mo_tf.py --input_model E：\ Googlenet \ inception_inference_graph.pb --input_shape（1,224,224,3）--mean_value [103.94,116.78,123.68] --scale 1 --data_type FP16 --output_dir E：\ Googlenet
Model Optimizer参数：
通用参数：
        -输入模型的路径：E：\ Googlenet \ inception_inference_graph.pb-
        生成的IR的路径：E：\ Googlenet
        -IR输出名称：inception_inference_graph-
        日志级别：错误
        -批处理：未指定，继承自模型
        -输入层：未指定，继承自模型
        -输出层：未指定，从模型继承
        -输入形状：（1,224,224,3）
        -平均值：[103.94,116.78,123.68]
        -比例值：未指定
        -比例因子：1.0-
        红外精度：FP16-
        启用融合：True-
        启用分组卷积融合：True-
        将平均值移至预处理部分：False-
        反向输入通道：False
TensorFlow特定参数：
        -文本protobuf格式的输入模型：False
        -TensorBoard的模型转储路径：无
        -具有TensorFlow自定义层实现的共享库列表：无
        -使用输入/输出节点名称更新配置文件：无
        -使用带有对象检测API的用于生成模型的配置文件：无
        -要卸载的操作：无
        -要卸载的模式：无
        -使用配置文件：无
模型优化器版本：2019.2.0-436-gf5827d4
警告：在标志解析进入stderr之前进行记录。
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连续的轴[0，2，3] E1209 16：39：37.163681 18460 ReduceToPooling.py:85] 不支持不连续的轴[0，2，3]
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连续的轴[0，2，3] E1209 16：39：37.215543 18460 ReduceToPooling.py:85] 不支持不连续的轴[0，2，3]
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E1209 16：39：37.216540 18460 ReduceToPooling.py:85]不支持
不连续轴[0，2，3]缩小E1209 16：39：37.217537 18460 ReduceToPooling.py:85]不连续轴[0，2]缩小，3]不支持
E1209 16：39：37.219533 18460 ReduceToPooling.py:85] 不支持不连续轴进行缩小[0，2，3] E1209 16：39：37.222525 18460 ReduceToPooling.py:85]不支持
缩小不支持
连续轴[0，2，3] E1209 16：39：37.223521 18460 ReduceToPooling.py:85] 不支持不连续轴[0，2，3]
E1209 16：39：37.223521 18460 ReduceToPooling.py ：85]不支持以不连续的轴进行缩小[0，2，3]
E1209 16：39：37.224533 18460 ReduceToPooling.py:85]不支持
不连续的轴[0，2，3]缩小E1209 16：39：37.225516 18460 ReduceToPooling.py:85]不连续的轴[0，2]缩小，3]不支持
E1209 16：39：37.225516 18460 ReduceToPooling.py:85] 不支持不连续轴进行缩小[0，2，3] E1209 16：39：37.226513 18460 ReduceToPooling.py:85]不支持
缩小不支持
连续的轴[0，2，3] E1209 16：39：37.226513 18460 ReduceToPooling.py:85] 不支持不连续的轴[0，2，3]
E1209 16：39：37.227511 18460 ReduceToPooling.py ：85]不支持以不连续的轴进行缩小[0，2，3]
E1209 16：39：37.229506 18460 ReduceToPooling.py:85]不支持
不连续轴[0，2，3]缩小E1209 16：39：37.229506 18460 ReduceToPooling.py:85]不连续轴[0，2]缩小，3]不支持
E1209 16：39：37.232498 18460 ReduceToPooling.py:85] 不支持不连续轴进行缩小[0，2，3] E1209 16：39：37.232498 18460 ReduceToPooling.py:85]不支持
不缩小不支持
连续的轴[0，2，3] E1209 16：39：37.233495 18460 ReduceToPooling.py:85] 不支持不连续的轴[0，2，3]
E1209 16：39：37.233495 18460 ReduceToPooling.py ：85]不支持以不连续的轴进行缩小[0，2，3]
E1209 16：39：37.234492 18460 ReduceToPooling.py:85]不支持不连续轴进行缩小[0，2，3]

[ SUCCESS ] Generated IR model.
[ SUCCESS ] XML file: E:\Googlenet\inception_inference_graph.xml
[ SUCCESS ] BIN file: E:\Googlenet\inception_inference_graph.bin
[ SUCCESS ] Total execution time: 59.75 seconds

 

After converting to IR, use the following command to perform calculation stick reasoning, and then report an error.

 

E:\Googlenet>python classification_sample.py -m inception_inference_graph.xml -i  E:\dataset\1\end_data\000002.jpg -d MYRIAD
[ INFO ] Loading network files:
        inception_inference_graph.xml
        inception_inference_graph.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image E:\dataset\1\end_data\000002.jpg is resized from (317, 500) to (224, 224)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
Traceback (most recent call last):
  File "classification_sample.py", line 152, in <module>
    sys.exit(main() or 0)
  File "classification_sample.py", line 102, in main
    exec_net = plugin.load(network=net)
  openvino.inference_engine.ie_api.IEPlugin.load中的文件“ ie_api.pyx”，第551行openopeno.inference_engine.ie_api.IEPlugin.load中的
  文件“ ie_api.pyx”，行561
RuntimeError：[VPU]数据37587_const不支持的精度I32

我该如何解决这个问题，非常感谢！

 

 

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2 Replies
yu__jia
Beginner
‎12-09-2019 05:05 PM
517 Views

Hello,every,I can convert pb to IR with the following command.

python mo_tf.py --input_model E:\Googlenet\inception_inference_graph.pb --input_shape (1,224,224,3) --mean_value [103.94,116.78,123.68] --scale 1 --data_type FP16 --output_dir E:\Googlenet
Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      E:\Googlenet\inception_inference_graph.pb
        - Path for generated IR:        E:\Googlenet
        - IR output name:       inception_inference_graph
        - Log level:    ERROR
        - Batch:        Not specified, inherited from the model
        - Input layers:         Not specified, inherited from the model
        - Output layers:        Not specified, inherited from the model
        - Input shapes:         (1,224,224,3)
        - Mean values:  [103.94,116.78,123.68]
        - Scale values:         Not specified
        - Scale factor:         1.0
        - Precision of IR:      FP16
        - Enable fusing:        True
        - Enable grouped convolutions fusing:   True
        - Move mean values to preprocess section:       False
        - Reverse input channels:       False
TensorFlow specific parameters:
        - Input model in text protobuf format:  False
        - Path to model dump for TensorBoard:   None
        - List of shared libraries with TensorFlow custom layers implementation:        None
        - Update the configuration file with input/output node names:   None
        - Use configuration file used to generate the model with Object Detection API:  None
        - Operations to offload:        None
        - Patterns to offload:  None
        - Use the config file:  None
Model Optimizer version:        2019.2.0-436-gf5827d4
WARNING: Logging before flag parsing goes to stderr.
E1210 09:00:32.872642   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
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E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.950715   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported
E1210 09:00:32.966336   964 ReduceToPooling.py:85] Reduce with not consecutive axes [0, 2, 3] is not supported

[ SUCCESS ] Generated IR model.
[ SUCCESS ] XML file: E:\Googlenet\inception_inference_graph.xml
[ SUCCESS ] BIN file: E:\Googlenet\inception_inference_graph.bin
[ SUCCESS ] Total execution time: 57.19 seconds.

After converting to IR, use the following command to perform calculation stick reasoning, and then report an error.

python classification_sample.py -m inception_inference_graph.xml -i  E:\dataset\1\end_data\000002.jpg -d MYRIAD
[ INFO ] Loading network files:
        inception_inference_graph.xml
        inception_inference_graph.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image E:\dataset\1\end_data\000002.jpg is resized from (317, 500) to (224, 224)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
Traceback (most recent call last):
  File "classification_sample.py", line 152, in <module>
    sys.exit(main() or 0)
  File "classification_sample.py", line 102, in main
    exec_net = plugin.load(network=net)
  File "ie_api.pyx", line 551, in openvino.inference_engine.ie_api.IEPlugin.load
  File "ie_api.pyx", line 561, in openvino.inference_engine.ie_api.IEPlugin.load
RuntimeError: [VPU] Unsupported precision I32for data 37099_const

What should I do. Thank you very much. 

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Luis_at_Intel
Moderator
‎12-10-2019 03:40 PM
517 Views

Hi yu, jia

The error you are observing seems to be related to an unsupported precision of FP32 for the Myriad plugin, the plugin only supports FP16 but I can see that you converted the model using the correct precision so not sure what could be causing this. If you can share your model I could attempt to convert it, you can share your model privately in case you don't want to post it here. 

I have tried converting the InceptionV1 model (inception_v1_2016_08_28.tar.gz) from the link you provided following the Example of an Inception V1 Model Conversion guide, it seems to be working fine for CPU/MYRIAD devices. I can also see that you are using an older version of the toolkit, if possible it would be great if you can try using the latest release of OpenVINO (R3.1). 

 

Command I used to convert this PB to IR:

python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model ./inception_v1_inference_graph.pb --input_checkpoint ./inception_v1.ckpt -b 1 --mean_value [127.5,127.5,127.5] --scale 127.5 --data_type FP16 --output_dir ./fp16

Test model with classification_sample.py and NCS:

python3 /opt/intel/openvino/deployment_tools/inference_engine/samples/python_samples/classification_sample/classification_sample.py -m fp16/inception_v1_inference_graph.xml -i soccer-ball.jpg -d MYRIAD --labels fp16/labels.txt 

[ INFO ] Creating Inference Engine
[ INFO ] Loading network files:
    fp16/inception_v1_inference_graph.xml
    fp16/inception_v1_inference_graph.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image soccer-ball.jpg is resized from (318, 360) to (224, 224)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Processing output blob
[ INFO ] Top 10 results: 
Image soccer-ball.jpg

classid probability
------- -----------
 ball     0.9941406
 ball    0.0027180
891:volleyball0.0009551
 ball    0.0006504
 ball    0.0003643
600:honeycomb0.0001680
 ball    0.0001532
430:baseball0.0001435
72:scorpion0.0001428
bra, bandeau0.0001181

 

Regards,

Luis

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